Dynamic measurements using digital image correlation
Bibliographic record
Abstract
Digital image correlation (DIC), which enables non-contact measurement of displacements and strains, has seen widespread adoption within the geotechnical physical modelling community for the measurement of static displacements. Advances in high temporal resolution cameras now permit the use of DIC to calculate accelerations. However, it is currently unclear how the image acquisition rate and the choice of DIC algorithm influence the quality of this data. This paper describes the sources of error that affect the dynamic measurement accuracy. Numerical and physical experiments are used to demonstrate the relevance of (a) bias error in the sub-pixel interpolation scheme, (b) the ratio of sample rate to the frequency of the signal being monitored and (c) the signal-to-noise ratio on the accuracy and precision of DIC acceleration measurements. The results demonstrate that by using appropriate image texture, sampling frequencies and signal-to-noise ratios, measurements with an accuracy similar to accelerometers can be achieved. The displacement measurement error due to bias errors was found to be 0·0015 pixels. The error in the calculated velocity and acceleration was a function of the amplitude of displacement measurements with an optimum ratio between the sampling frequency to the signal frequency found to be between 25 and 50.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".